The moderation effects of mobile technology advancement and system barrier on m-commerce channel preference behavior

Abstract

Mobile commerce, or m-commerce, has quickly become a powerful, indispensable approach for diverse business to consumer (B2C) industries to secure technology-oriented, risk-taking N-generation customers. However, their behavioral preferences have not been investigated in-depth, as most practitioners believe m-commerce consumers are merely a frontier group of e-commerce consumers, and not unique service consumers with distinct characteristics. This study examines m-commerce consumers’ unique behavioral aspects by conducting thorough theoretical research based on an integrated framework that includes the IS success, web satisfaction, and B2C channel preference models. We first construct an integrated model to analyze the fundamental roles of information system quality in an m-commerce context. A set of hypotheses are then tested and studied to verify the moderation effects of ubiquity, localization, personalization (mobile attributes), and cognitive effort (system barriers) on the newly established relationships. The statistical results are obtained using a survey data of 503 consumers with m-commerce service experiences in Korea, a leading m-commerce country. Finally, the results are analyzed and interpreted to identify m-commerce consumers’ perceived service quality levels, as well as their comparative differences against e-commerce consumers. We believe that both researchers and practitioners will benefit from this research, in that it not only isolates a prioritized list of key determinants of m-commerce success, but also highlights the necessity of continuous research effort regarding future market orientations.

Appendices

Appendix 1: Construct measures and sources for the questionnaire

All items were measured using a 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. General questions within each construct were not used in the Cronbach’s alpha (α) computation. Inter-item correlations are reported for two-item factors. (^aConstruct dropped due to low loading on factor in confirmatory factor analysis; ^bCronbach’s alphas were computed after dropping the items with low factor loadings).

Perceived value (Adapted from Parasuraman et al. 2005) [α = 0.82^b, interitem correlation = 0.70]

PV1:

The overall convenience of using this m-commerce channel (MC)

PV2:

The extent to which the MC gives you a feeling of being in control

PV3^a :

The prices of the products and services available at this MC (how economical MC is)

PV4^a :

The overall value you get from this MC for your money and effort

Satisfaction (Adapted from Ding et al. 2011) [α = 0.85]

SAT1:

I truly enjoyed purchasing via MC

SAT2:

My choice to purchase via MC was a wise one

SAT3:

I am satisfied with my purchase using MC

Channel preference (Adapted from Devaraj et al. 2002) [α = 0.87^b]

CPREF1:

I strongly recommend MC to others

CPREF2:

For products I can buy online, I intend to completely switch over to MC (from EC)

CPREF3:

I intend to increase my use of MC in the future

CPREF4^a :

I plan to use MC again

Information quality (Adapted from McKinney et al. 2002) [α = 0.94]

Based on your experience of using m-commerce, please provide your evaluation of its performance in terms of the following features. The provided m-commerce information is:

Relevance [α = 0.78]

REL1:

Applicable to your purchase decision

REL2:

Related to your purchase decision

REL3:

Pertinent to your purchase decision

REL4:

In general, information is relevant to your purchase decision

Understandability [α = 0.82]

UND1:

Clear in meaning

UND2:

Easy to comprehend

UND3:

Easy to read

UND4:

In general, information is understandable for you in making purchase decisions

Reliability [α = 0.88]

RELB1:

Trustworthy

RELB2:

Accurate

RELB3:

Credible

RELB4:

In general, information is reliable for making your purchase decision

Adequacy [α = 0.84]

ADE1:

Sufficiency of your purchase decision

ADE2:

Completion of your purchase decision

ADE3:

Information that contains necessary topics for your purchase decision

ADE4:

In general, is specific information adequate for your purchase decision

Scope ^a

SCO1:

Information that covers a wide range (range of information)

SCO2:

Information that contains a wide variety of topics (level of detail provided)

SCO3:

In general, information covers a broad scope for your purchase decision

Usefulness [α = 0.87]

USE1:

Informative to your purchase decision

USE2:

Valuable to your purchase decision

USE3:

In general, information is useful in your purchase decision

System quality (Adapted from McKinney et al. 2002) [α = 0.95]

Based on your experience of using m-commerce, please provide your evaluation of its performance in terms of the following features. The performance of the m-commerce system is:

Access [α = 0.72]

ACC1:

Responsive to your request

ACC2:

Quickly loads all the text and graphics

ACC3:

In general, MC system provides good access

Usability [α = 0.88]

USA1:

A simple layout for its contents

USA2:

Easy to use

USA3:

Well organized

USA4:

A clear design

USA5:

In general, MC system is user-friendly

Entertainment [α = 0.90]

ENT1:

Visually attractive

ENT2:

Fun to navigate

ENT3:

Interesting to navigate

ENT4:

In general, MC system is entertaining

Hyperlinks [α = 0.89]

HYP1:

An adequate number of links

HYP2:

Clear descriptions for each link

HYP3:

In general, MC system has appropriate hyperlinks

Navigation [α = 0.84]

NAV1:

It is easy to go back and forth between pages

NAV2:

Provides a few clicks to locate information

NAV3:

In general, MC system is easy to navigate

Interactivity ^a

INT1:

Provides the capability to create a list of selected items (such as a shopping cart)

INT2:

Provides the capability to change items from a created list (such as changing the contents of a shopping cart)

INT3:

Provides the capability to create a customized product (such as computer configuration or creating clothes appropriate to your taste and measurements)

INT4:

In general, MC system can actively participate in creating your desired product

Mobile technology attributes and system barrier

Personalization (Adapted from Wang and Li 2012) [α = 0.84]

PERS1:

I feel that my personal needs have (relatively) been met when using MC

PERS2:

I feel that my MC Service provider has the (relatively) same norms and values that I have

PERS3:

My MC Service provider (relatively better) provides me with information regarding its services, according to my preferences

PERS4:

There are numerous ways to submit inquiries to my MC service provider

Localization (Adapted from Wang and Li 2012) [α = 0.83^b, interitem correlation = 0.71]

LOC1^a :

My MC Service provider is capable of identifying my location and providing me with the on-the-spot information I need

LOC2^a :

My MC Service provider can accurately locate me using GPS or information stored on my mobile device

LOC3:

I can receive personalized marketing information (e.g., shopping offers, advertisements, coupons, etc.) from my MC Service provider

LOC4:

My MC Service provider can offer me timely and location-specific packets of information

Ubiquity (Adapted from Tojib and Tsarenko 2012 and Choi et al. 2008) [α = 0.92]

UBI1:

I can use MC anytime

UBI2:

I can use MC anywhere

UBI3:

I can use MC when needed

UBI4:

I can access MC while traveling

Cognitive effort (Adapted from Kleijnen et al. 2007) [α = 0.77^b, interitem correlation = 0.63]

COG1^a :

MC will likely be uncomplicated to use (reverse-scored)

COG2:

MC will likely require much effort to understand its use

COG3:

I believe it will be difficult to learn how MC works

Appendix 2: Results of full moderating effects model

Relationships within integrated model

Personalization (H8)a

Localization (H9)a

Ubiquity (H10)b

Cognitive effort (H11)a

Low

High

Low

High

Low

High

Low

High

IQ → SAT

−0.07

−0.03

(NS)

−0.13

0.27*

(S*)

0.02

0.13

(NS)

0.13

0.04

(NS)

IQ → PV

−0.05

0.47**

(S*)

0.53**

0.10

(NS)

0.33*

−0.03

(NS)

0.18

0.45**

(NS)

PV → CPREF

0.04

0.11

(NS)

0.12

−0.05

(NS)

0.20**

−0.01

(NS)

0.23**

0.01

(S*)

SAT → CPREF

1.01***

0.99***

(NS)

0.87***

1.09***

(NS)

0.81***

1.29***

(NS)

0.99***

0.95***

(NS)

SQ → SAT

0.31**

0.36**

(NS)

0.41***

0.23*

(NS)

0.56***

0.15

(S**)

0.22*

0.47***

(NS)

SQ → PV

0.48***

0.30**

(NS)

0.30**

0.50**

(NS)

0.39***

0.52***

(NS)

0.57***

0.32**

(NS)

PV → SAT

0.34***

0.47***

(NS)

0.40***

0.50***

(NS)

0.38***

0.47***

(NS)

0.55***

0.38***

(NS)

1. (S), supported; (NS), not supported
2. * Significant at 0.10 level; ** Significant at 0.05 level; *** Significant at 0.01 level
3. ^aAccepted based on two-step analysis testing; ^b rejected based on two-step analysis testing